def register_uc_5_6_callbacks(app, plot_service) -> None:
"""
Register all UC-5.6 callbacks with Dash app.
Parameters
----------
app : Dash
Dash application instance.
plot_service : PlotService
Singleton PlotService instance (shared across all callbacks).
Notes
-----
- Registers panel toggle and network diagram rendering callbacks
- Refer to official documentation for processing logic details
"""
logger.info("[UC-5.6] Registering callbacks")
# ========================================
# Callback 1: Toggle Informative Panel
# ========================================
@app.callback(
Output("uc-5-6-collapse", "is_open"),
Input("uc-5-6-collapse-button", "n_clicks"),
State("uc-5-6-collapse", "is_open"),
prevent_initial_call=True,
)
def toggle_uc_5_6_info_panel(n_clicks: Optional[int], is_open: bool) -> bool:
"""Toggle UC-5.6 informative panel collapse state."""
if n_clicks:
logger.debug(f"[UC-5.6] Toggling info panel: {is_open} -> {not is_open}")
return not is_open
return is_open
# ========================================
# Callback 2: Render Network Diagram
# ========================================
@app.callback(
Output("uc-5-6-chart", "children"),
[
Input("merged-result-store", "data"),
Input("uc-5-6-accordion-group", "active_item"),
],
prevent_initial_call=True,
)
def render_uc_5_6(
merged_data: Optional[Dict[str, Any]], active_item: Optional[str]
) -> html.Div:
"""
Render UC-5.6 compound-compound similarity network on accordion activation.
Parameters
----------
merged_data : dict, optional
Dictionary containing merged result data with 'biorempp_df' key.
active_item : str, optional
Active accordion item ID.
Returns
-------
html.Div
Container with chart or error message.
Notes
-----
- Validates merged_data structure and extracts BioRemPP DataFrame
- Maps column names flexibly (compoundname/genesymbol with aliases)
- Cleans data removing nulls and placeholder values
- Passes prepared data to NetworkStrategy via PlotService
- Computes compound-compound similarity based on shared genes
- Generates similarity network diagram
"""
logger.info(f"[UC-5.6] Render triggered, active_item: {active_item}")
# Check if accordion is active
if active_item != "uc-5-6-accordion":
logger.debug("[UC-5.6] Accordion not active. Skipping render.")
raise PreventUpdate
try:
# ========================================
# Step 1: Validate merged_data structure
# ========================================
if not merged_data:
logger.warning("[UC-5.6] merged_data is None or empty")
return _create_error_message(
"No data available. Please upload and process data first.",
"bi bi-exclamation-triangle",
)
if not isinstance(merged_data, dict):
logger.error("[UC-5.6] merged_data is not a dictionary")
return _create_error_message(
"Invalid data structure. Please reload the application.",
"bi bi-x-circle",
)
if "biorempp_df" not in merged_data:
logger.error("[UC-5.6] merged_data does not contain 'biorempp_df' key")
return _create_error_message(
"BioRemPP data not found. This use case requires "
"BioRemPP database.",
"bi bi-database-x",
)
# ========================================
# Step 2: Extract DataFrame
# ========================================
logger.debug("[UC-5.6] Extracting DataFrame from merged_data")
biorempp_data = merged_data["biorempp_df"]
if not biorempp_data:
logger.warning("[UC-5.6] biorempp_df is empty")
return _create_error_message(
"BioRemPP dataset is empty. Please check your input data.",
"bi bi-inbox",
)
df = pd.DataFrame(biorempp_data)
if df.empty:
logger.warning("[UC-5.6] DataFrame is empty after conversion")
return _create_error_message(
"No data available after processing.", "bi bi-inbox"
)
logger.info(
f"[UC-5.6] Processing DataFrame: {len(df)} rows, "
f"{len(df.columns)} columns"
)
logger.debug(f"[UC-5.6] Available columns: {df.columns.tolist()}")
# ========================================
# Step 3: Map column names flexibly
# ========================================
col_map = {}
# Compound name column
compound_candidates = [
"compoundname",
"Compound_Name",
"compound_name",
"CompoundName",
"Compound",
"compound",
"compoundID",
"Compound_ID",
]
for col_name in compound_candidates:
if col_name in df.columns:
col_map["compoundname"] = col_name
logger.debug(f"[UC-5.6] Mapped compoundname to '{col_name}'")
break
# Gene symbol column
gene_candidates = [
"genesymbol",
"Gene_Symbol",
"gene_symbol",
"GeneSymbol",
"Gene",
"gene",
"geneName",
"gene_name",
]
for col_name in gene_candidates:
if col_name in df.columns:
col_map["genesymbol"] = col_name
logger.debug(f"[UC-5.6] Mapped genesymbol to '{col_name}'")
break
# ========================================
# Step 4: Validate required columns found
# ========================================
required = ["compoundname", "genesymbol"]
missing_cols = [col for col in required if col not in col_map]
if missing_cols:
logger.error(
f"[UC-5.6] Missing columns: {missing_cols}. "
f"Available: {df.columns.tolist()}"
)
return _create_error_message(
f"Required columns not found: {', '.join(missing_cols)}. "
f"Available columns: {', '.join(df.columns[:5])}...",
"bi bi-exclamation-octagon",
)
# ========================================
# Step 5: Prepare data for strategy
# ========================================
df_for_plot = df[[col_map["compoundname"], col_map["genesymbol"]]].rename(
columns={
col_map["compoundname"]: "compoundname",
col_map["genesymbol"]: "genesymbol",
}
)
# Clean data
initial_count = len(df_for_plot)
df_for_plot = df_for_plot.dropna()
# Strip whitespace and remove placeholders
for col in df_for_plot.columns:
df_for_plot[col] = df_for_plot[col].astype(str).str.strip()
df_for_plot = df_for_plot[
~df_for_plot["compoundname"].isin(
["#N/D", "#N/A", "N/D", "", "nan", "None"]
)
]
df_for_plot = df_for_plot[
~df_for_plot["genesymbol"].isin(
["#N/D", "#N/A", "N/D", "", "nan", "None"]
)
]
cleaned_count = len(df_for_plot)
logger.info(
f"[UC-5.6] Data cleaned: {initial_count} -> {cleaned_count} rows"
)
if df_for_plot.empty:
return _create_error_message(
"No valid compound-gene data found after cleaning.", "bi bi-funnel"
)
# Log statistics
n_compounds = df_for_plot["compoundname"].nunique()
n_genes = df_for_plot["genesymbol"].nunique()
logger.info(
f"[UC-5.6] Data statistics: "
f"{n_compounds} unique compounds, {n_genes} unique genes"
)
# ========================================
# Step 6: Generate plot using PlotService
# ========================================
logger.debug("[UC-5.6] Calling PlotService to generate similarity network")
fig = plot_service.generate_plot(
use_case_id="UC-5.6",
data=df_for_plot,
filters=None,
force_refresh=False,
)
logger.info("[UC-5.6] Similarity network generation successful")
# ========================================
# Step 7: Prepare download filename and return chart component
# ========================================
try:
suggested = sanitize_filename(
"UC-5.6", "compound_compound_network", "png"
)
except Exception:
suggested = "compound_compound_network.png"
base_filename = os.path.splitext(suggested)[0]
return html.Div(
[
# Statistics summary
html.Div(
[
html.Small(
[
html.I(className="bi bi-info-circle me-2"),
f"Network built from {n_compounds} compounds sharing "
f"{n_genes} genes",
],
className="text-muted",
)
],
className="mb-2",
),
# Graph
dcc.Graph(
id="uc-5-6-graph",
figure=fig,
config={
"displayModeBar": True,
"displaylogo": False,
"responsive": True,
"modeBarButtonsToRemove": ["pan2d", "lasso2d", "select2d"],
"toImageButtonOptions": {
"format": "svg",
"filename": base_filename,
"height": 1000,
"width": 1200,
"scale": 6,
},
},
style={"height": "900px", "width": "100%"},
className="mt-3",
),
]
)
except ValueError as ve:
logger.error(
f"[UC-5.6] ValueError during processing: {str(ve)}", exc_info=True
)
return _create_error_message(
f"Data validation error: {str(ve)}", "bi bi-exclamation-triangle"
)
except Exception as e:
logger.error(f"[UC-5.6] Unexpected error: {str(e)}", exc_info=True)
return _create_error_message(
f"An unexpected error occurred: {str(e)}", "bi bi-bug"
)
logger.info("[UC-5.6] All callbacks registered successfully")